RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fields

Chen, Gaoxiang; Li, Qun; Shi, Fuqian; Rekik, Islem; Pan, Zhifang

doi:10.1016/j.neuroimage.2020.116620

Cited by 60 publications

(27 citation statements)

References 31 publications

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“…The major drawback of convolutional neural network models (CNN) lies in the fuzzy segmentation outcomes and the spatial information reduction caused by the strides of convolutions and pooling operations 32 . To further improve the segmentation accuracy and efficiency, several advanced strategies have been applied to obtain better segmentation results 21 , 25 , 33 , 34 with approaches like dilated convolution/pooling 35 – 37 , skip connections 38 , 39 , as well as additional analysis and new post-processing modules like Conditional Random Field (CRF) and Hidden Conditional Random Field (HCRF) 10 , 40 , 41 . Using the dilated convolution method causes a large receptive field to be used without applying the pooling layer to the aim of relieving the issue of information loss during the training phase.…”

Section: Methodsmentioning

confidence: 99%

“…As an initial step in this kind of segmentation, the key information is extracted from the input image using some feature extraction algorithm, and then a discriminative model is trained to recognize the tumor from normal tissues. The designed machine learning techniques generally employ hand-crafted features with various classifiers, such as random forest 10 , support vector machine (SVM) 11 , 12 , fuzzy clustering 3 . The designed methods and features extraction algorithms have to extract features, edge-related details, and other necessary information—which is time-consuming 13 .…”

Section: Introductionmentioning

confidence: 99%

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Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

Ranjbarzadeh

Kasgari

Ghoushchi

et al. 2021

Sci Rep

384

129

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Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

Ranjbarzadeh

Kasgari

Ghoushchi

et al. 2021

Sci Rep

384

129

View full text Add to dashboard Cite

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“…Decision trees are a class of widely used machine learning algorithms, which achieve state-of-the-art performance in many tasks including in lesion and tissue segmentation. [22][23][24][25][26][27] The first GBDT was a LightGBM regressor (C1 reg ) trained on features of the brain on a per parcellated brain region basis, whereas the second GBDT was a LightGBM classifier (C2 vox ) trained on voxelwise brain features in standard Montreal Neurological Institute 152 (MNI152) space. The workflow is shown in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

“…We developed a computational method of lesion detection, comprising two stacked gradient boosting decision tree (GBDT) LightGBM (Microsoft Corporation, Redmond, Washington, USA) 21 classifiers/regressors. Decision trees are a class of widely used machine learning algorithms, which achieve state‐of‐the‐art performance in many tasks including in lesion and tissue segmentation 22‐27 . The first GBDT was a LightGBM regressor (C1 reg ) trained on features of the brain on a per parcellated brain region basis, whereas the second GBDT was a LightGBM classifier (C2 vox ) trained on voxelwise brain features in standard Montreal Neurological Institute 152 (MNI152) space.…”

Section: Methodsmentioning

confidence: 99%

“…The DESPOT PD, DESPOT T1, and susceptibility weighted scans were not further utilized for C2 vox , as these contrasts, having been used by C1 reg , were not found to bring any additional benefit to C2 vox in our preliminary investigations. The selected contrasts were all nonrigidly Age at disease onset a Not applicable 15 [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] 14 [7][8][9][10][11][12][13][14][15][16][17][18][19] Disease duration a Not applicable 20 18 [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] a Given in years as median [interquartile range].…”

Section: C2 Vox (The Voxelwise Abnormality Detector)mentioning

confidence: 99%

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Detection of covert lesions in focal epilepsy using computational analysis of multimodal magnetic resonance imaging data

et al. 2021

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Objective To compare the location of suspect lesions detected by computational analysis of multimodal magnetic resonance imaging data with areas of seizure onset, early propagation, and interictal epileptiform discharges (IEDs) identified with stereoelectroencephalography (SEEG) in a cohort of patients with medically refractory focal epilepsy and radiologically normal magnetic resonance imaging (MRI) scans. Methods We developed a method of lesion detection using computational analysis of multimodal MRI data in a cohort of 62 control subjects, and 42 patients with focal epilepsy and MRI‐visible lesions. We then applied it to detect covert lesions in 27 focal epilepsy patients with radiologically normal MRI scans, comparing our findings with the areas of seizure onset, early propagation, and IEDs identified at SEEG. Results Seizure‐onset zones (SoZs) were identified at SEEG in 18 of the 27 patients (67%) with radiologically normal MRI scans. In 11 of these 18 cases (61%), concordant abnormalities were detected by our method. In the remaining seven cases, either early seizure propagation or IEDs were observed within the abnormalities detected, or there were additional areas of imaging abnormalities found by our method that were not sampled at SEEG. In one of the nine patients (11%) in whom SEEG was inconclusive, an abnormality, which may have been involved in seizures, was identified by our method and was not sampled at SEEG. Significance Computational analysis of multimodal MRI data revealed covert abnormalities in the majority of patients with refractory focal epilepsy and radiologically normal MRI that co‐located with SEEG defined zones of seizure onset. The method could help identify areas that should be targeted with SEEG when considering epilepsy surgery.

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HRCUNet: Hierarchical Region Contrastive Learning for Segmentation of Breast Tumors in DCE‐MRI

He,

Luo,

Peng

et al. 2024

Concurrency and Computation

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Segmenting breast tumors from dynamic contrast‐enhanced magnetic resonance images is a critical step in the early detection and diagnosis of breast cancer. However, this task becomes significantly more challenging due to the diverse shapes and sizes of tumors, which make it difficult to establish a unified perception field for modeling them. Moreover, tumor regions are often subtle or imperceptible during early detection, exacerbating the issue of extreme class imbalance. This imbalance can lead to biased training and challenge accurately segmenting tumor regions from the predominant normal tissues. To address these issues, we propose a hierarchical region contrastive learning approach for breast tumor segmentation. Our approach introduces a novel hierarchical region contrastive learning loss function that addresses the class imbalance problem. This loss function encourages the model to create a clear separation between feature embeddings by maximizing the inter‐class margin and minimizing the intra‐class distance across different levels of the feature space. In addition, we design a novel Attention‐based 3D Multi‐scale Feature Fusion Residual Module to explore more granular multi‐scale representations to improve the feature learning ability of tumors. Extensive experiments on two breast DCE‐MRI datasets demonstrate that the proposed algorithm is more competitive against several state‐of‐the‐art approaches under different segmentation metrics.

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RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fields

Cited by 60 publications

References 31 publications

Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

Detection of covert lesions in focal epilepsy using computational analysis of multimodal magnetic resonance imaging data

HRCUNet: Hierarchical Region Contrastive Learning for Segmentation of Breast Tumors in DCE‐MRI

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